Effectiveness of Different Approaches to Disseminating Traveler Information on Travel Time Reliability (2013)

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

156 a P P e N d I x a overview In cities where congestion in the transportation system is commonplace, drivers are accustomed to the congestion; they expect and plan for some increase in travel time, particularly during peak driving times. Many system users either adjust their schedules to avoid peak hours or budget extra time to allow for unexpected traffic congestion or incidents. However, problems arise when travel times are much higher than antici- pated. Most travelers are less tolerant of unexpected travel time increases because those longer travel times cause travelers to be late for work or important meetings, to miss appointments, or to incur extra child-care fees. Moreover, unexpected delays in the transportation of goods by a freight carrier or shipper can result in disruption in just-in-time delivery and manufactur- ing processes and can cause the carrier or shipper to lose money and a competitive edge (Texas A&M Transportation Institute with Cambridge Systematics, Inc. 2006). Transportation professionals most commonly discuss travel time reliability in terms of historical average travel times cal- culated over periods of a year or longer, as illustrated in Fig- ure A.1. A typical definition for travel time reliability is the following: The consistency or dependability in travel times, as measured from day to day or across different times of the day. Most travelers do not experience the same average travel time each day, however. As shown in Figure A.2, travelers experi- ence and remember something much different than the aver- age throughout a year of commutes. Their travel times may vary greatly from day to day, and they remember the few bad days they suffered through unexpectedly longer travel times. Research has shown that travel time reliability information can provide transportation system users with a more com- plete picture of the expected travel time along a particular route. The challenge is how to communicate that reliability information effectively to road and transit system users so that they understand it clearly. Another example illustrating travel time reliability is shown in Figure A.3, which shows travel time data from a major com- muter route in Seattle, Washington. Without congestion along the route, travel times are about 12 min (e.g., see President’s Day in Figure A.3). On all other weekdays, the average travel time is 18 min. But when traffic incidents and weather com- bine to cause unexpected congestion, travel times may be 25 min or more, or 39%, longer than usual. Commuters who travel this route must plan for this variability if they want to arrive on time. If they plan their commute on the basis of the average travel time, they will be late half the time and early the other half of the time. In other words, commuters have to build in a time cushion or buffer to their trip planning to account for the variability. If they build in a buffer, they will arrive early on some days. That is not necessarily a bad thing, but the extra time is still carved out of their day—time they could be using on pursuits other than commuting. Travel Time reliability Metrics The measurement of travel time reliability is an emerging practice. However, a few measures appear to have technical merit and are thought to be easily understood by nontechni- cal audiences. Most of these measures compare high travel time days with average travel time days. Four recommended measures are 90th or 95th percentile travel time, buffer index, planning time index, and frequency with which congestion exceeds an expected threshold (Texas A&M Transportation Institute with Cambridge Systematics, Inc. 2006). The 90th or 95th percentile travel time is a time identified for a specific travel route and indicates how bad the delay will be on the heaviest travel days (Texas A&M Transportation Institute with Cambridge Systematics, Inc. 2006). These travel times are reported in minutes and are thought to be easily understood by commuters familiar with their trips. Therefore, General Literature Review

157 delays and to ensure on-time arrival (Texas A&M Transpor- tation Institute with Cambridge Systematics, Inc. 2006). The buffer index is expressed as a percentage and its value increases as reliability gets worse. For example, a buffer index of 40% means that, for a 20-min average travel time, a traveler should budget an additional 8 min (20 min × 40% = 8 min) to ensure on-time arrival most of the time. In this example, the eight extra minutes is called the buffer time. The buffer index is com- puted as the difference between the 95th percentile travel time and average travel time, divided by the average travel time. this measure is ideally suited for traveler information. It has the disadvantage of not being easily compared across trips, because most trips will have different lengths. Nor can this mea- sure be used to easily combine route or trip travel times into a subarea or citywide average. Two indices that enable comparisons or combinations of routes or trips with different lengths are the buffer index and the planning time index. The buffer index represents the extra time cushion (or buffer) that most travelers add to their aver- age travel time when planning trips to account for unforeseen Source: Texas A&M Transportation Institute with Cambridge Systematics, Inc. 2006. Jan DecJuly Travel Time How traffic conditions have been communicated Annual average Figure A.1. Average travel time used by professionals. Jan DecJuly Travel Time What travelers experience … Travel times vary greatly day-to-day … and what they remember Source: Texas A&M Transportation Institute with Cambridge Systematics, Inc. 2006. Figure A.2. Traveler travel time experiences. Source: Cambridge Systematics, Inc. with Texas Transportation Institute 2005. State Route 520 Eastbound, Seattle, 5 to 6 pm 0 5 10 15 20 25 30 January February March April Weekdays in 2003 T ra ve l T im e (m in u te s) Martin Luther King Day President's Day 2 incidents with rain 3 incidents 1 incident with rain 4 incidents 1 incident rain Free-flow travel time = 12 minutes Average travel time = 18 minutes Figure A.3. Example of commuters planned trips based on the worst days, not the average day.

158 Importance of Travel Time reliability Travel time reliability is significant to many transportation system users, whether they are vehicle drivers, transit riders, freight shippers, or even air travelers. Good and consistent sys- tem reliability is a valuable service that can be provided on pri- vately operated and publicly operated highways alike. Because reliability is so important to transportation system users, trans- portation planners, operators, and decision makers should consider travel time reliability a key performance measure. Travel Time Reliability and Highway Travel Travel time reliability is valuable to traffic professionals because it better quantifies the benefits of traffic management and operation activities than simple averages. For example, con- sider a typical before-and-after study that attempts to quantify the benefits of an incident management or ramp metering program. The improvement in average travel time may appear modest, as shown on the left side of Figure A.4. However, reli- ability measures will show a much greater improvement— as illustrated on the right side of Figure A.4—because they show the effect of improving the worst few days of unexpected delay and will be much more meaningful to the transportation system users. For drivers, travel time reliability information can be valu- able when they are selecting a route. For example, the value of travel time reliability was assessed through a mail survey, trip diaries, and loop-detector data by Lam and Small (2001) soon after the first high-occupancy/toll (HOT) lane opened on State Route 91 in Riverside, California. The researchers found that, for women in this study, the value of travel time reliabil- ity was actually higher than simple travel time information. The planning time index represents the total travel time that a traveler should expect or plan on when an adequate buffer time is included (Texas A&M Transportation Institute with Cambridge Systematics, Inc. 2006). The planning time index differs from the buffer index in that it includes typical delay as well as unexpected delay. Thus, the planning time index compares near-worst-case travel time to a travel time in light or free-flow traffic. For example, a planning time index of 1.60 means that, for a 15-min trip in light traffic, the total time that should be planned for the trip is 24 min (15 min × 1.60 = 24 min). The planning time index is useful because it can be directly compared with the travel time index (a mea- sure of average congestion) on similar numeric scales. The planning time index is computed as the 95th percentile travel time divided by the free-flow travel time. From a data perspective, continuous travel time data is the only way to establish reliability patterns empirically. Although predictive methods (e.g., the SHRP 2 L03 project Analytic Procedures for Determining the Impacts of Reliability Mitiga- tion Strategies) may be used in a reliability monitoring system when the data are unavailable, only continuously collected travel time data can produce the actual travel time distribu- tion from which all reliability metrics are derived. For exam- ple, the reliability metrics being used in the SHRP 2 L03 project, as shown in Table A.1, are all derivatives of the statistical dis- tribution of travel times. At present, transportation experts do not agree on the terms to be used or what the mathematical calculations of each term should be. If transportation professionals can’t come to con- sensus on the technical terms, then the general public certainly will not do so. The purpose of the L14 project is to determine what terms the layperson uses to refer to travel time reliability concepts and to encourage the use of those terms in commu- nications with transportation system users. Table A.1. Recommended Reliability Performance Metrics from SHRP 2 Project L03 Reliability Performance Metric Definition Unit Buffer index (BI), mean-based The difference between the 95th percentile travel time and the average travel time, normalized by the average travel time Percent Buffer index, median-based The difference between the 95th percentile travel time and the median travel time, normalized by the median travel time Percent Failure or on-time measures, median-based Percentage of trips with travel times less than 1.10 × median travel time and/or 1.25 × median travel time Percent Failure or on-time measures, speed-baseda Percentage of trips with travel times less than 50 mph, 45 mph, and/or 30 mph Percent Misery index (modified) The average of the top 5% worst travel times divided by the free-flow travel time None Planning time indices 95th, 90th, and 80th percentile travel times divided by the free-flow travel time None Skew statistic The ratio of (90th percentile travel time minus the median) to (the median minus the 10th percentile) None a Speed is the space-mean speed over the study section. Source: Cambridge Systematics, Inc. (2007a).

159 for a transit vehicle as being longer than an equivalent amount of time spent riding in the vehicle. Real-time information that allows transit riders to schedule their own arrival at a transit stop and/or to monitor the wait time remaining until the vehi- cle’s arrival increases rider confidence in the service (Perk et al. 2008). Transit passengers surveyed in two cities ranked knowl- edge of when their bus would arrive and knowledge that it would arrive on time as the two most important factors affect- ing their decision to ride transit (Peng et al. 2002). Travel Time Reliability and Freight In terms of economic value, reliability is probably more impor- tant to freight carriers and shippers than to personal travelers. With the rise in just-in-time deliveries (largely as a replacement for extensive warehousing), providing dependable (reliable) service has become extremely valuable while failure to provide dependable service can increase costs considerably (Cambridge Systematics, Inc. 2007a). For example, improvements in trans- portation reliability play an important role in reducing inven- tory in the chemical supply chain for freight shippers. Because of the many nodes in the supply chain, upwards of one-third of all chemical inventory is in transit at any point in time. Inventory managers keep safety or buffer supplies to cushion against variability of inbound arrivals, and the amount of safety supplies increases with the degree of unreliability and the number of stocking locations (Cambridge Systematics, Inc. 2007a). However, the capacity to receive chemical supplies is limited by the size of the liquid storage silos. Balancing capacity with demand is a challenge. As transportation reli- ability decreases, wait time, dead freight, and cost increases (Cambridge Systematics, Inc. 2006). real-Time Travel Information: State of the Practice Real-time travel time messages have been in use in the United States for well over a decade, ever since traffic monitoring and integration systems became available and reliable. The most commonly used media for these messages are dynamic message For men, the value of time was roughly 50% higher than the value of reliability information. The reasons for this difference were not clear from the data collected, though some have inter- preted the data to indicate that women have more time critical commitments related to child-care trips. For this study, the researchers defined travel time as the 90th percentile travel time minus the median travel time. The authors discuss further how the transponder usage records of participants show that few drivers habitually used the HOT lane. Rather, people made the decision whether to pay for the HOT lane on a daily basis depending on trip purpose and traffic conditions. In applica- tions such as HOT lanes, travel time reliability information may be useful en route to help drivers make the purchase deci- sion to use the HOT lanes. The influence of pre-trip and en route travel information on route decisions has been dem- onstrated in other studies: An evaluation of the Washington State DOT’s 511 travel information system in 2005 found that 21% of respondents changed their original travel plans on the basis of information they got from the 511 system (PRR, Inc. 2005). Drivers on an Orlando, Florida, toll road who stated that they used information from the state’s 511 service or from dynamic message signs (DMSs) that displayed estimated delay times for the road were more likely to change their route in response to unexpected congestion. A review of research on travel time and travel time reli- ability conducted by the Center of Urban Transportation Research (University of South Florida) includes the finding that most travelers value trip time reliability at least as much as actual trip time. In fact, when travelers’ arrival and depar- ture times were inflexible because of the nature of the trip, the value of reliability was as much as three times that of trip time (Concas and Kolpakov 2009). Travel Time Reliability and Transit Studies of transit ridership have shown that trip time reliabil- ity (including the reliability of a rider’s wait time at transit stops) is more important to retaining riders than the trip and waiting times themselves. Wait-time reliability is particularly important, as transit riders tend to perceive time spent waiting Source: Texas A&M Transportation Institute with Cambridge Systematics, Inc. 2006. 2003 20052004 Travel Time Small improvement in average travel times Before After Average day 2003 20052004 Travel Time Larger improvement in travel time reliability Before After Worst day of the month Figure A.4. Reliability measures capture the benefits of traffic management.

160 Portland area using travel time ranges such as “12–15 MIN” (FHWA 2005a). Ranges of travel times are also used by Ten- nessee’s SmartWay intelligent transportation system in the Nashville and Knoxville areas (FHWA 2005b), by the Illinois State Toll Highway Authority on Chicago-area highways (FHWA 2005c), and by the Texas DOT in the San Antonio area (FHWA 2005d). Travel time ranges are just one way to express the travel time reliability of a highway segment. A California PATH pro- gram study in 2009 found that most commuters surveyed (71%) preferred that travel time estimates be displayed as an exact number of minutes rather than as a range of minutes (29%) (Ban et al. 2009). A similar preference was found among Arizona commuters. On the basis of commuter feedback, the Arizona DOT changed the format of its travel time estima- tion signs to provide “to the minute” precision rather than 5-min estimation windows (Phoenix Tightens Travel Time Estimates 2008). A study of DMS messaging performed by Battelle for the FHWA in 2004 recommended including the distance in miles along with travel time. The study found that distance infor- mation is particularly useful to travelers who are unfamiliar with the area and enables them to mentally estimate the amount of delay from the distance coupled with the esti- mated travel time (PBS&J 2004). Real-time travel time messaging tends to be most effective on a road on which travel times are likely to change with rea- sonable frequency. If travel times are too static, drivers tend to view the messages as static rather than dynamic and there- fore less credible (Meehan 2005). This “freshness factor” may hold true for travel time reliability information as well. Some agencies such as Houston TranStar provide a time stamp (e.g., “Travel time – to US 59 – 6 min at 10:10”) to their travel time signs and web-based information to assure users that the information is current (Houston TranStar 2012a). Messages on DMSs on highways in the United Kingdom (UK) change from travel time estimates (number of miles and minutes to given destinations under normal traffic flow condi- tions) to travel delay descriptions and estimates (e.g., “Segment name – Accident – 15 minutes delay”) in response to roadway incidents. Delay time estimates are based on “typical traffic pro- files” of individual road segments according to time of day and known traffic generators (Traffic England Traffic Map 2012). Some agencies have started to show comparative travel times to certain destinations via different routes. The Wash- ington State DOT recently installed new travel time signs in the Seattle area showing side-by-side travel time estimates for two different routes to a common destination (Washington State DOT 2012a). Signs such as this would be natural places to add information about travel time reliability along the two routes. Signs showing comparative travel times in general- purpose and HOT lanes are another location where travel signs (DMSs) and transportation agency websites; but the widespread use of cell phones and other mobile devices is prompting a growing number of transportation agencies and providers to offer real-time updates on transportation condi- tions and options via e-mails, text messages, and Twitter feeds. Real-time travel time estimates are most often provided for a particular roadway segment or a particular transit route on the basis of recent travel speeds or conditions. Some agencies also provide travel time comparisons among two or more routes/roadways to help travelers make decisions about the route or transportation mode to take. Most recent and most rare are the information sources that advise travelers about travel time reliability—that is, the likelihood that the esti- mated travel time for a particular trip or trip segment will be dependable. The following subsections describe some of the real-time travel information messages that are being provided to travelers on DMSs, on websites, and via mobile devices, as well as some of the lessons learned about providing travel information. Dynamic Message Signs Two Department of Transportation surveys of state and local agencies in 2007 found that incident reports were the most common form of real-time traffic information provided to travelers in large metropolitan areas in the United States, fol- lowed by travel times and then travel speeds (U.S. Govern- ment Accountability Office 2009). Dudek reported in 2008 that travel time information was displayed on DMSs by 18 depart- ments of transportation (DOTs) in the United States. He cited two primary reasons why travel time information was not dis- played on CMSs by some state DOTs: (1) infrastructure or soft- ware was not available and (2) congestion was not a problem (Dudek 2008). The Georgia DOT began posting travel time messages on DMSs in 1998, using the qualitative descriptors “moving very well,” “moving well,” “moving slowly,” and “moving very slowly.” Responding to requests from Georgia drivers for more precise terminology, the Georgia DOT used its travel management software (NaviGAtor) to generate approximate travel times along roadway segments. Drivers now see a three- line message: the name of a destination (such as a highway exit), the distance to that destination in miles, and a travel time range based on the average speed along the roadway seg- ment. The Georgia DOT does not post travel times for dis- tances greater than 15 miles, because the accuracy of the time range decreases at greater distances (Dudek 2008). Other signs in the Atlanta region alert drivers that travel time infor- mation can be obtained by dialing 511 anywhere in the state (NaviGAtor 511 Real-Time Traffic Map 2012). DMSs of the type developed for the Georgia DOT are also being used by the Oregon and Tennessee DOTs. The Oregon DOT provides travel time estimates on highways in the

161 arterials before freeway entrance ramps to provide drivers with information to make route choices (Peng et al. 2004). A DMS pilot program in the San Francisco Bay Area provides trav- elers with both highway and Caltrain (transit) travel times to selected destinations, along with the arrival time of the next train (Mortazivi 2009). Real-time bus and train arrival information is available in increasing numbers of U.S. cities, posted on DMS at transit centers and on transit websites. Some transit providers also provide real-time notifications about route delays and diver- sions. A real-time train arrival sign at one of Washington, D.C.’s, Metro stations is shown in Figure A.6. Real-time arrival signs tend to be viewed positively by tran- sit customers. Customer surveys conducted by transit agen- cies in the United States and abroad found that real-time arrival information at transit stops made riders feel more confident, particularly at night, and even improved riders’ overall perception of the quality of transit service provided (Schweiger 2003). Travel Websites Many state DOT transportation management centers (TMCs) and partner transportation agencies provide users with real- time travel information via websites. The format and features of these websites vary considerably. For example, the Califor- nia Department of Transportation (Caltrans) reproduces the travel time information displayed on DMS on Los Angeles area freeways via its TMC website (California Department of Transportation District 7 2012). The Tennessee SmartWay website (Tennessee Department of Transportation 2012) and the Utah DOT CommuterLink time reliability could be added and could prove useful to motorists making route decisions during a trip. Consider- ation must be given, however, to the amount of information that drivers can read while passing a sign. The 2009 DMS study recommended that travel times for HOV lanes and general-purpose lanes not be provided together on one sign, as this could result in too much information for drivers to process during the time they have to look at the sign (Ban et al. 2009). This recommendation may be mitigated by other conclusions of the study, including the finding that travelers who see travel time messages on DMSs on their regular route can begin to anticipate elements of the messages and there- fore read and understand them in less time than they would otherwise need (Ban et al. 2009). The first travel time signs in use for a managed lane facility were on a HOT lane along I-15 in San Diego. Research has shown that users consistently overestimate their travel time savings (Brownstone and Small 2009). For this reason, agen- cies have been reluctant to post travel times in managed lanes for fear that, when actual comparative travel times are shown, drivers might not choose to use the managed lane. Other research has shown, however, that drivers value the trip time reliability offered by managed lanes (Lam and Small 2001). The Long Island Expressway uses the words Average Travel Time on their signs showing travel times to multiple destina- tions along a single route (see Figure A.5). However, the origi- nal request to FHWA to deviate from the Manual on Uniform Traffic Control Devices showed signs with the term Estimated Travel Time (FHWA 2004). The presentation of travel time is not limited to highways and highway travel. The Wisconsin DOT provides highway travel times to specified destinations via the freeway on selected Source: FHWA 2004. Figure A.5. Northern state parkway signs on Long Island. Source: TTI. Figure A.6. Next-train arrival sign at a Washington, D.C., Metro station.

162 congestion (traffic moving at 55 mph or higher), with moder- ate traffic congestion (54 mph to 35 mph), and with heavy traffic congestion (34 mph to 15 mph) (RoadStats, LLC 2012). The Washington State DOT has recently added a feature to its travel time website that displays 95th percentile travel times (Washington State DOT 2012a). A user enters an original-destination pair from a drop-down menu containing names of suburbs, and the system displays a text message as shown in Figure A.7. The Driving Times feature on the San Francisco Bay Area’s 511 website also allows users to enter the origin and destination of their driving trip; the web- site then generates multiple potential routes for the trip, dis- playing the current and typical/historical trip times for each route, along with a table of minimum, maximum, and aver- age current traffic speeds (and typical historical speed) on each of the route’s roadway segments (see Figure A.8). The site’s Predict-a-Trip feature allows users to view the typical traffic speeds and travel times of the same route options for some future trip by entering the day and time period (511 SF Bay 2012). Many airlines now provide on-time performance histories for particular flight numbers and times that can be viewed by customers making online reservations. In addition, third- party websites compile information from multiple airlines and airports to provide estimates, or forecasts, about a flight’s on-time performance. The FlightCaster website tracks both current delays and historical on-time performance for U.S. domestic flights to estimate a specific flight’s departure time; six delay factors are also shown on the forecast, with color- coded icons to signal potential problems (FlightCaster 2012). Similarly, the FlightStats website shows historical on-time per- formance information for airline routes using the named cat- egories on-time, late, very late, excessive, cancelled, and diverted along with the percentage of flights in each category. The per- centages are also shown on a bar graph (FlightStats 2012). website (Utah Commuterlink 2012) both provide real-time travel information to online users by posting real-time pho- tos of the travel time DMS signs, as well as color-coded high- way maps showing road conditions (hazardous, patches of ice/snow, flooded), traffic flow, incident and construction locations and descriptions, and real-time camera views of highway locations. The UK highways website features the same types of information and also provides advance notifi- cation of future construction sites and expected future events (such as holiday travel) that are likely to affect roadway con- ditions and traffic speeds (Traffic England Traffic Map 2012). The roadway information website provided by the Ontario, Canada, Ministry of Transportation displays a similar traffic map, with green, yellow, and red traffic flow categories labeled “moving well (75 km/h and above),” “moving slowly (40 to 75 km/h),” and “very slow (less than 40 km/h)” (Ontario Ministry of Transportation 2012). Travel time reliability information is starting to make appearances on transportation websites. The Wisconsin DOT website provides a table of current and “normal” travel times for Milwaukee-area highways. Travel times that are 20% or more above normal are shown in bold print (Kothuri et al. 2007). The Washington State DOT provides a similar trip time table, also including times for the same roadway segments if the HOV lane is used (Washington State DOT 2012b). The travel information website for the Gary-Chicago- Milwaukee corridor also displays a table of current and average travel times and traffic speeds for highways along the corridor (RoadStats, LLC 2012). The user can click on the average travel time number for each segment to view a graph detailing the most recently collected travel time, the average travel time for all historical data samples, and the normal range of travel time values by time period over a 24-hour period each day. The graph also includes three speed thresholds, indicating what the travel time would be for the segment with no traffic Figure A.7. Travel time reliability display from Washington State DOT website.

163 Commission of Southern Nevada 2012). The Arkansas State Highway Department has begun using Twitter to notify motor- ists about highway conditions (Arkansas State Highway and Transportation Department 2012). The Washington Metropolitan Area Transit Authority’s (WMATA) “e-alerts” provide information about service delays or disruptions on Washington, D.C.’s, Metrorail, MetroAccess paratransit services, and elevators at Metro’s tran- sit centers (Washington Metropolitan Area Transit Authority 2012). WMATA has also begun to broadcast these alerts via Twitter, though soon after the new medium was adopted, sub- scribers realized they were receiving only partial messages. The partial messages were caused by limitations on Twitter message length; the longer messages that had been developed for an e-mail format were being truncated. WMATA is looking for ways to provide the same information to its Twitter subscribers using shorter messages (Hohmann 2009). The Bay Area Rapid Transit system in San Francisco provides real-time service information to its passengers via its mobile website (for those with access to an Internet connection), emailed and text-messaged service advisories, and most recently via Twitter updates (Rhodes 2009). Boston’s “T Alerts” provide the same service for passengers on Massachusetts Bay Trans- portation Authority buses and trains (Massachusetts Bay Transportation Authority 2012). Communicating reliability Information: other research The challenge with conveying travel time reliability informa- tion to the user is ensuring that they understand the message. Cognitive science has shown that most people are not good at understanding statistical concepts and applying them to everyday situations such as medical diagnoses, gambling odds, and variability in stochastic processes such as traffic (Gal 2002). Statistical literacy is related to overall aptitude with numbers, literacy, and cultural components. Research has shown significant cultural differences in understanding statistical concepts, and those related to risk in particular (Wright et al. 1978). Communicating probabilities or risks using only qualita- tive language can lead to misunderstandings, simply because the listener (or reader) may ascribe a different meaning to a descriptive word than was intended. The English language has a multitude of terms for concepts of uncertainty and risk, but attempts to systematically map them to numerical prob- abilities have failed (Teigen 1988). Research has shown that people switch between numerical (e.g., 50-50 chance) and verbal (e.g., probably) in unpredictable ways controlled more by grammar than by probability values (Wallsten et al. 1993). In one study, tests of various probability terms (e.g., certainly, Route-by-route reliability information is generated by many transit systems for planning purposes, but is only rarely provided as part of transit customer information. Rutgers University in New Jersey has posted on-time performance history information for its bus routes, including percentages for on-time, early, and late arrivals (Rutgers Department of Transportation 2012). More transit systems may follow, espe- cially if traveler demand for this information grows. Evidence of increasing demand for transit reliability information includes the provision of a performance dashboard for TriMet transit routes in the Portland region (TriMet 2012). E-mails, Texts, Tweets: Mobile Device Messaging In addition to accessing the Caltrans website for travel times in the Los Angeles area, motorists may also subscribe to a free service that provides the same information on their mobile device. Similarly, Houston TranStar offers free, personalized e-mail alerts to its system users regarding incidents and travel times on Houston area freeways. The alerts can be sent to any device capable of receiving e-mail or text messages, including personal computers, mobile phones, personal digital assistants, and text pagers (Houston TranStar 2012b). A similar messag- ing service is provided by the Regional Transportation Com- mission of Southern Nevada’s Freeway and Arterial System of Transportation (FAST) program (Regional Transportation Source: 511 SF Bay 2012. Figure A.8. Driving times and traffic speeds for input origin–destination pair in San Francisco Bay Area.

164 probabilities related to cancer screenings as a set of frequen- cies rather than as a set of percentages resulted in quicker and more accurate comprehension of those probabilities by study participants, particularly if several probabilities had to be considered in tandem (Hanoch 2004). People presented with quantitative health risk information in pictograph formats perceived the information more accu- rately when it was presented in one compound graph (in which the proportions and percentages of the potential out- comes add up to 100%) than when the same information was presented as two side-by-side graphs (Price et al. 2007). The goal for establishing a lexicon to convey travel time reliability information is quite similar to that of establishing a lexicon to convey downstream traffic state information (i.e., what are traffic conditions downstream?) on a DMS, which TTI researchers addressed in the late 1970s. Through a series of standard and innovative laboratory experiments, research- ers developed and tested numerous text and graphical repre- sentations of downstream traffic conditions (Dudek et al. 1978). Research results showed that traffic descriptors should be displayed only for unusual traffic conditions (e.g., due to an accident). Displaying traffic descriptors during normal, recurrent peak-period traffic conditions was discouraged. In addition, the acceptable ways of conveying traffic state informa- tion were different in small cities than in large cities (Dudek and Huchingson 1986). As seen in the state-of-the-practice examples, a variety of terms are currently being used to describe travel times and the likelihood or reliability of those times. Average, historical, 95% reliable, and typical are just some of the terms used, and they may have different meanings to drivers depending on the context in which they are used. A variety of formats is also seen for the estimated travel times, as previously discussed. Early studies warned practition- ers about the presentation of travel time information (whether in terms of actual times, delays, time saved, etc.) because of the potential for the information to be refuted by travelers and thus reduce credibility of the system with drivers. More recent research suggests that drivers recognize (to some degree) the inherent variability and potential for change in travel time information (Dudek et al. 2000). Furthermore, such variance does not reduce the information’s credibility among drivers, nor does it reduce the desire for such infor- mation. As an example, both of the formats of travel time displays shown in Figure A.9 were equally understood to imply an approximate travel time that may not be exactly what is experienced by the driver reading that information at a particular point along the route. As mentioned previously, some drivers prefer the single time value format (e.g., 20 min) over a range of times, even though they know that time may vary somewhat. definitely, possibly, probably, rarely) with adolescents and young adults indicated that individual definitions of the terms were not consistent enough to convey information effectively to the general public. Absolute numbers, such as percentages or percentage ranges, were recommended instead of qualitative language (Biehl and Halpern-Felsher 2001). Some suggestions and recommendations for communicat- ing risk and probability to the public come from two non- transportation fields: weather forecasting and medicine. Although most people are familiar with weather forecasts on television and in other media, the probabilities used in those forecasts (e.g., 20% chance of rain) are not widely under- stood. In a study comparing several weather report formats, 43% of participants correctly interpreted a weather forecast that included symbolic icons depicting a weather condition (such as rain) and graphs showing the percent likelihood of that condition. When forecast information included graphs that showed the chance of rain AND the chance of “no rain,” the number of participants correctly understanding the forecast rose to 52% (Schwartz 2009). An experiment con- ducted with university students in the United Kingdom found that participants who were given a graph of forecast temperatures that included information about the probabil- ity, or uncertainty, of those temperatures answered questions about the forecast more accurately than the participants who were given the temperature graph by itself (BBC News 2007). A medical diagnosis or a decision about possible courses of treatment usually involves probabilistic data (the probability that a test result is accurate, the likelihood of various out- comes of a treatment). In a 2003 article for the British Medical Journal, several techniques were recommended for helping patients understand the risks and benefits associated with medical treatments: • Avoid the use of purely descriptive terms; supplement qualitative language with numbers. • Use a consistent denominator/numerical scale. • Provide both positive and negative outcomes (e.g., 3% chance of negative outcome AND 97% chance of positive outcome). • Express probabilities as absolute numbers (75% of cases have Outcome A, 25% have Outcome B) rather than in relative terms (three times as many cases have Outcome A as have Outcome B . . .). • Use visual aids such as pie charts and graphs to illustrate probabilities (Paling 2003). Studies examining both doctors’ and patients’ comprehen- sion of probability-based information have found that many people understand frequencies (e.g., 19 out of 20) bet- ter than percentages or proportions (95% or 0.95). Presenting

165 Dudek, C. L., and R. D. Huchingson. 1991. Use of Changeable Message Signs for Displaying Travel Time and Other Time-Related Messages. Dudek & Associates, Bryan, Tex. Dudek, C. L., and R. D. Huchingson. 1986. Manual on Real-Time Motorist Information Displays. FHWA, U.S. Department of Trans- portation, Washington, D.C. Dudek, C. L., R. D. Huchingson, R. J. Koppa, and M. L. Edwards. 1978. Human Factors Requirements for Real-Time Motorist Information Dis- plays. FHWA, U.S. Department of Transportation, Washington, D.C. Dudek, C., N. Trout, S. Booth, and G. L. Ullman. 2000. Improved Dynamic Message Sign Messages and Operations. Texas A&M Trans- portation Institute, College Station, Tex. FHWA. 2005a. Travel Time Messaging on Dynamic Message Signs— Portland, OR. U.S. Department of Transportation, Washington, D.C. http://ops.fhwa.dot.gov/publications/travel_time_study/portland/ portland_ttm.htm. Accessed Oct. 22, 2012. FHWA. 2005b. Travel Time Messaging on Dynamic Message Signs— Nashville, TN. U.S. Department of Transportation, Washington, D.C. http://ops.fhwa.dot.gov/publications/travel_time_study/nashville/ nashville_ttm.htm. Accessed Oct. 22, 2012 FHWA. 2005c. Travel Time Messaging on Dynamic Message Signs— Chicago, IL. U.S. Department of Transportation, Washington, D.C. http://ops.fhwa.dot.gov/publications/travel_time_study/chicago/ chicago_ttm.htm. Accessed Oct. 22, 2012. FHWA. 2005d. Travel Time Messaging on Dynamic Message Signs— Houston, TX. U.S. Department of Transportation, Washington, D.C. http://ops.fhwa.dot.gov/publications/travel_time_study/houston/ houston_ttm.htm. Accessed Oct. 22, 2012. FHWA. 2004. Request for Interpretation: 2-55(I)—CMS Display of Travel Time. Manual on Uniform Traffic Control Devices (MUTCD). U.S. Department of Transportation, Washington, D.C. May 17. http://mutcd.fhwa.dot.gov/resources/interpretations/pdf/2_551 .pdf. Accessed Oct. 22, 2012. FlightCaster. 2012. FlightCaster Inc. http://www.flightcaster.com/. Accessed Oct. 12, 2012. FlightStats. 2012. Historical On-time Performance Rating. FlightStats, Inc. http://www.flightstats.com/go/FlightRating/flightRatingBy Route.do. Accessed Oct. 29, 2012. Gal, I. 2002. Adults’ Statistical Literacy: Meanings, Components, Responsibilities. International Statistical Review, Vol. 70, No. 1. Hanoch, Y. 2004. Improving Doctor-Patient Understanding of Proba- bility in Communicating Cancer-Screening Test Findings. Journal of Health Communication, Vol. 9, pp. 327–335. Hohmann, J. 2009. Metro Tweets Far from Short and Sweet (or Deci- pherable). Washington Post, Aug. 21. http://www.washingtonpost .com/wp-dyn/content/article/2009/08/20/AR2009082004250_pf .html. Accessed Oct. 29, 2012. Houston TranStar. 2012a. Houston TranStar Dynamic Message Signs. http://traffic.houstontranstar.org/dms/dms_by_roadway.html. Accessed Oct. 12, 2012. Houston TranStar. 2012b. Houston TranStar Traffic Alerts. http://traffic .houstontranstar.org/trafficalert/. Accessed Oct. 12, 2012. Kothuri, S., K. Tufte, H. Hagedorn, R. L. Bertini, and D. Deeter. 2007. Survey of Best Practices in Real Time Travel Time Estimation and Prediction. Compendium of Technical Papers, Institute of Transporta- tion Engineers, District 6 Annual Meeting. Institute of Transportation Engineers, Portland, Ore. Lam, T., and K. A. Small. 2001. The Value of Time and Reliability: Mea- surement from a Value Pricing Experiment. Transportation Research Part E, Vol. 37, pp. 231–251. Massachusetts Bay Transportation Authority. 2012. Welcome to T-Alerts. http://www.talerts.com/. Accessed Oct. 12, 2012. Bibliography 511 SF Bay. 2012. http://traffic.511.org/. Accessed Oct. 12, 2012. Arkansas State Highway and Transportation Department. 2012. Road Conditions. http://www.arkansashighways.com/roads/roads.aspx. Accessed Oct. 12, 2012. Ban, X., Y. Li, and J. Margulici. 2009. Optimal Use of Changeable Message Signs for Displaying Travel Times. University of California, Berkeley, Calif. BBC News. 2007. Predictable Forecast . . . Probably? BBC News. Oct. 29. http://news.bbc.co.uk/2/hi/programmes/more_or_less/7067003 .stm. Accessed Oct. 11, 2012. Biehl, M., and B. Halpern-Felsher. 2001. Adolescents’ and Adults’ Under- standing of Probability Expressions. Journal of Adolescent Health, Vol. 28, pp. 30–35. Brownstone, D., and K. Small. 2009. Valuing Time and Reliability: Assess- ing the Evidence from Road Pricing Demonstrations. Transportation Research Part A: Policy and Practice, Vol. 39, No. 4, pp. 279–293. California Department of Transportation District 7. 2012. Travel Times Information. http://www.dot.ca.gov/dist07/travel/travel_times/index .php. Accessed Oct. 29, 2012. Cambridge Systematics, Inc. 2007a. SHRP 2 Project L03: Analytic Proce- dures for Determining the Impacts of Reliability Mitigation Strategies. Transportation Research Board of the National Academies, Wash- ington, D.C. Cambridge Systematics, Inc. 2007b. Identification of the Reliability Perfor- mance Metrics to Be Used. Transportation Research Board of National Academies, Washington, D.C. Cambridge Systematics, Inc. 2006. AASHTO Freight Transportation Bottom Line Report: Freight Demand and Logistics. AASHTO, Wash- ington, D.C. Cambridge Systematics, Inc., with Texas Transportation Institute. 2005. Traffic Congestion and Reliability: Trends and Advanced Strategies for Congestion Mitigation. FHWA, U.S. Department of Transporta- tion, Washington, D.C. http://ops.fhwa.dot.gov/congestion_report/ congestion_report_05.pdf. Accessed Oct. 22, 2012. Concas, S., and A. Kolpakov. 2009. Synthesis of Research on Value of Time and Value of Reliability. Center for Urban Transportation Research, University of South Florida, Tampa, Fla. Dudek, C. L. 2008. Changeable Message Sign Displays During Non-Incident, Non-Roadwork Periods. NCHRP, Transportation Research Board, National Research Council, Washington, D.C. Source: Dudek and Huchingson 1991. Figure A.9. Comprehended travel time displays.

166 Rutgers Department of Transportation. 2012. On-Time Performance Stats. Rutgers, The State University of New Jersey, New Brunswick, N.J. http://parktran.rutgers.edu/ontimeschedule.shtml. Accessed Oct. 12, 2012. Schwartz, J. 2009. People’s Misperceptions Cloud Their Understand- ing of Rainy Weather Forecasts. EurekAlert. April 14. http://www .eurekalert.org/pub_releases/2009-04/uow-pmc041409.php. Accessed Oct. 11, 2012. Schweiger, C. 2003. Real-Time Bus Arrival Information Systems: A Syn- thesis of Transit Practice. Transportation Research Board, Wash- ington, D.C. Tailor, P. 2006. Journey Time Reliability and Network Monitoring. High- ways Agency, Department for Transport, London. Teigen, K. 1988. The Language of Uncertainty. Acta Psychologica, Vol. 68, pp. 27–28. Tennessee Department of Transportation. 2012. TDOT SmartWay. http://www.tdot.state.tn.us/tdotsmartway/. Oct. 12, 2012. Texas A&M Transportation Institute with Cambridge Systematics, Inc. 2006. Travel Time Reliability: Making It There On Time, All the Time. FHWA, U.S. Department of Transportation, Washington, D.C. http://ops.fhwa.dot.gov/publications/tt_reliability/brochure/ index.htm. Accessed Oct. 11, 2012. Traffic England. 2012. UK Highways Agency. http://www.trafficengland .com/index.aspx. Accessed Oct. 12, 2012. Traffic England Traffic Map. 2012. UK Highways Agency. http://www .trafficengland.co.uk/map.aspx?isTrafficAlert=true&lat=53.357971 &lon=-2.333874. Accessed Oct. 22, 2012. TriMet. 2012. Performance Dashboard. TriMet. http://trimet.org/about/ dashboard.htm. Accessed Oct. 29, 2012. U.S. Government Accountability Office. 2009. Surface Transportation: Efforts to Address Highway Congestion Through Real-Time Traffic Information Systems Are Expanding but Face Implementation Chal- lenges. U.S. Government Accountability Office, Washington, D.C. http://www.gao.gov/assets/100/96486.pdf. Accessed Oct. 12, 2012. Utah Commuterlink. 2012. Utah Department of Transportation. http:// www.utahcommuterlink.com. Accessed Oct. 12, 2012. Wallsten, T., D. V. Budescu, R. Zwick, and S. M. Kemp. 1993. Prefer- ences and Reasons for Communicating Probabilistic Information in Verbal or Numerical Terms. Bulletin of the Psychonomic Society, Vol. 31, No. 2, pp. 135–138. Washington Metropolitan Area Transit Authority. 2012. Alerts & Adviso- ries. http://www.wmata.com/rider_tools/metro_service_status/rail_ Bus.cfm?. Accessed Oct. 12, 2012. Washington State Department of Transportation. 2012a. Seattle Area Travel Times. http://www.wsdot.com/traffic/Seattle/traveltimes/ 95reliable.aspx. Accessed Oct. 12, 2012. Washington State Department of Transportation. 2012b. Best Time To Leave. http://www.travelmidweststats.com/. Accessed Oct. 29, 2012. Wright, G., L. D. Phillips, P. C. Whalley, G. T. Choo, K. Ng, I. Tan, et al. 1978. Cultural Differences in Probabilistic Thinking. Journal of Cross-Cultural Psychology, Vol. 9, No. 3, pp. 285–299. Meehan, B. 2005. Travel Times on Dynamic Message Signs. ITE Journal, Vol. 75, No. 9, pp. 23–27. Mortazivi, A. E. 2009. Commuter Travel Time Information System: Displaying Transit Messages on Changeable Message Signs. Pre- sented at 88th Annual Meeting of the Transportation Research Board (CD-ROM). Washington, D.C. NaviGAtor 511 Real-Time Traffic Map. 2012. Georgia Department of Transportation. http://www.georgia-navigator.com/realtimetraffic .html. Accessed Oct. 22, 2012. Ontario Ministry of Transportation. 2012. Interactive Map. http://whttp:// www.mto.gov.on.ca/english/traveller/trip/map.shtml?ll=43 .696424,-79.459648&z=10. Accessed Oct. 29, 2012. Paling, J. 2003. Strategies to Help Patients Understand Risks. British Medical Journal, Vol. 327, pp. 745–748. PBS&J. 2004. Amber, Emergency, and Travel Time Messaging Guidance for Transportation Agencies. FHWA, U.S. Department of Transpor- tation, Washington, D.C. http://www.ops.fhwa.dot.gov/TravelInfo/ resources/cms_rept/cmspractices.pdf. Peng, Z., N. Guequierre, and J. C. Blakeman. 2004. Motorist Response to Arterial Variable Message Signs. In Transportation Research Record: Journal of the Transportation Research Board, No. 1899, Transporta- tion Research Board of the National Academies, Washington, D.C., pp. 55–63. Peng, Z., D. Yu, and E. Beimborn. 2002. Transit User Perceptions of the Benefits of Automatic Vehicle Location. In Transportation Research Record: Journal of the Transportation Research Board, No. 1791, Transportation Research Board of the National Academies, Wash- ington, D.C., pp. 127–133. Perk, V., J. Flynn, and J. Volinski. 2008. Transit Ridership, Reliability, and Retention. National Center for Transit Research, University of South Florida, Tampa, Fla. Phoenix Tightens Travel Time Estimates. 2008. TrafficTechnologyToday .com. July 25. http://www.traffictechnologytoday.com/news.php? NewsID=7061. Accessed Oct. 22, 2012. Price, M., R. Cameron, and P. Butow. 2007. Communicating Risk Information: The Influence of Graphical Display Format on Quan- titative Information Perception—Accuracy, Comprehension and Preference. Patient Education and Counseling, Vol. 69, pp. 121–128. PRR, Inc. 2005. WSDOT 511 IVR Survey and Usability Testing Results. http://www.wsdot.wa.gov/NR/rdonlyres/596DD86C-340E-4B10- 8660-22805F9AE10E/0/511Survey.pdf. Accessed Oct. 11, 2012. Regional Transportation Commission of Southern Nevada. 2012. Free- way Traffic Alerts. http://www.rtcsnv.com/planning-engineering/ freeway-arterial-system-of-transportation-fast/freeway-traffic-alerts/. Accessed Oct. 12, 2012. Rhodes, S. 2009. Survey Finds Strong Demand for BART Mobile Applica- tions. Bay Area Rapid Transit. Feb. 20. http://www.bart.gov/news/ articles/2009/news20090126.aspx. Accessed Oct. 29, 2012. RoadStats, LLC. 2012. Travel Midwest Stats. GCM Travel Website. http://www.travelmidweststats.com/. Accessed Oct. 22, 2012.